2 citations · 4 across the 3 of their papers we have counts for
6 papers
An adaptive procedure for Fourier estimators: illustration to deconvolution and decompounding
Céline Duval, Johanna Kappus
We introduce a new procedure to select the optimal cutoff parameter for Fourier density estimators that leads to adaptive rate optimal estimators, up to a logarithmic factor. This…
Strong Gaussian approximation of the mixture Rasch model
Friedrich Liese, Alexander Meister, Johanna Kappus
We consider the famous Rasch model, which is applied to psychometric surveys when n persons under test answer m questions. The score is given by a realization of a random binary (n…
Nonparametric adaptive estimation for grouped data
Céline Duval, Johanna Kappus
The aim of this paper is to estimate the density f of a random variable X when one has access to independent observations of the sum of K 2 independent copies of X. We provid…
Nonparametric estimation for irregularly sampled Lévy processes
Johanna Kappus
We consider nonparametric statistical inference for Lévy processes sampled irregularly, at low frequency. The estimation of the jump dynamics as well as the estimation of the distr…
Density deconvolution from repeated measurements without symmetry assumption on the errors
Johanna Kappus, Fabienne Comte
We consider deconvolution from repeated observations with unknown error distribution. So far, this model has mostly been studied under the additional assumption that the errors are…
Adaptive nonparametric estimation for Lévy processes observed at low frequency
Johanna Kappus
This article deals with adaptive nonparametric estimation for Lévy processes observed at low frequency. For general linear functionals of the Lévy measure, we construct kernel esti…